Metadata-Version: 1.1
Name: daftlistings
Version: 1.8.1
Summary: A library that enables programmatic interaction with daft.ie. Daft.ie has nationwide coverage and contains about 80% of the total available properties in Ireland.
Home-page: https://github.com/AnthonyBloomer/daftlistings
Author: Anthony Bloomer
Author-email: ant0@protonmail.ch
License: MIT
Description: Daftlistings
        ============
        
        |Build Status| |codecov|
        
        A library that enables programmatic interaction with
        `Daft.ie <https://daft.ie>`__. Daft.ie has nationwide coverage and
        contains about 80% of the total available properties in Ireland.
        
        Installation
        ------------
        
        Daftlistings is available on the `Python Package Index
        (PyPI) <https://pypi.org/project/daftlistings/>`__. You can install
        daftlistings using pip.
        
        .. code:: bash
        
           virtualenv env
           source env/bin/activate
           pip install daftlistings
        
        To install the development version, run:
        
        .. code:: bash
        
           pip install https://github.com/AnthonyBloomer/daftlistings/archive/dev.zip
        
        Usage
        -----
        
        .. code:: python
        
           from daftlistings import Daft
        
           daft = Daft()
           listings = daft.search()
        
           for listing in listings:
               print(listing.formalised_address)
               print(listing.daft_link)
               print(listing.price)
        
        By default, the Daft ``search`` function iterates over each page of
        results and appends each Listing object to the array that is returned.
        If you wish to disable this feature, you can set ``fetch_all`` to
        ``False``:
        
        .. code:: python
        
           daft.search(fetch_all=False)
        
        Examples
        ~~~~~~~~
        
        Get apartments to let in Dublin City that are between €1000 and €1500
        and contact the advertiser of each listing.
        
        .. code:: python
        
           from daftlistings import Daft, RentType
        
           daft = Daft()
        
           daft.set_county("Dublin City")
           daft.set_listing_type(RentType.APARTMENTS)
           daft.set_min_price(1000)
           daft.set_max_price(1500)
        
           listings = daft.search()
        
           for listing in listings:
        
               contact = listing.contact_advertiser(
                   name="Jane Doe",
                   contact_number="019202222",
                   email="jane@example.com",
                   message="Hi, I seen your listing on daft.ie and I would like to schedule a viewing."
               )
               
               if contact:
                   print("Advertiser contacted")
        
        You can sort the listings by price, distance, upcoming viewing or date
        using the SortType object. The SortOrder object allows you to sort the
        listings descending or ascending.
        
        .. code:: python
        
        
           from daftlistings import Daft, SortOrder, SortType, RentType
        
           daft = Daft()
        
           daft.set_county("Dublin City")
           daft.set_listing_type(RentType.ANY)
           daft.set_sort_order(SortOrder.ASCENDING)
           daft.set_sort_by(SortType.PRICE)
           daft.set_max_price(2500)
        
           listings = daft.search()
        
           for listing in listings:
               print(listing.formalised_address)
               print(listing.daft_link)
               print(listing.price)
               features = listing.features
               if features is not None:
                   print('Features: ')
                   for feature in features:
                       print(feature)
               print("")
        
        Parse listing data from a given search result url.
        
        .. code:: python
        
        
           from daftlistings import Daft
        
           daft = Daft()
           daft.set_result_url("https://www.daft.ie/dublin/apartments-for-rent?")
           listings = daft.search()
        
           for listing in listings:
               print(listing.formalised_address)
               print(listing.price)
               print(' ')
        
        Find student accommodation near UCD that is between 850 and 1000 per
        month
        
        .. code:: python
        
           from daftlistings import Daft, SortOrder, SortType, RentType, University, StudentAccommodationType
        
           daft = Daft()
           daft.set_listing_type(RentType.STUDENT_ACCOMMODATION)
           daft.set_university(University.UCD)
           daft.set_student_accommodation_type(StudentAccommodationType.ROOMS_TO_SHARE)
           daft.set_min_price(850)
           daft.set_max_price(1000)
           daft.set_sort_by(SortType.PRICE)
           daft.set_sort_order(SortOrder.ASCENDING)
           daft.set_offset(offset)
           listings = daft.search()
        
           for listing in listings:
               print(listing.price)
               print(listing.formalised_address)
               print(listing.daft_link)
        
        Map the 2-bed rental properties in Dublin and color code them wrt to
        prices. Save the map in a html file.
        
        .. code:: python
        
           from daftlistings import Daft, SortOrder, SortType, RentType, MapVisualization
           import pandas as pd
        
           daft = Daft()
           daft.set_county("Dublin City")
           daft.set_listing_type(RentType.ANY)
           daft.set_sort_order(SortOrder.ASCENDING)
           daft.set_sort_by(SortType.PRICE)
           # must sort by price in asending order, MapVisualization class will take care of the weekly/monthly value mess
           daft.set_max_price(2400)
           daft.set_min_beds(2)
           daft.set_max_beds(2)
        
           listings = daft.search()
           properties = []
           print("Translating {} listing object into json, it will take a few minutes".format(str(len(listings))))
           print("Igonre the error message")
           for listing in listings:
               try:
                   if listing.search_type != 'rental':
                       continue
                   properties.append(listing.as_dict_for_mapping())
               except:
                   continue
        
        
           df = pd.DataFrame(properties)
           print(df)
        
           dublin_map = MapVisualization(df)
           dublin_map.add_markers()
           dublin_map.add_colorbar()
           dublin_map.save("dublin_apartment_to_rent_2_bed_price_map.html")
           print("Done, please checkout the html file")
        
        For more examples, check the `Examples
        folder <https://github.com/AnthonyBloomer/daftlistings/tree/dev/examples>`__
        
        Parallel as_dict()
        ~~~~~~~~~~~~~~~~~~
        
        lisitng.as_dict() is relatively slow for large volume of listings. Below
        is an exmple script using threading and joblib library technique to
        speedup this process
        
        .. code:: python
        
           from daftlistings import Daft, RentType
           from joblib import Parallel, delayed
           import time
        
           def translate_listing_to_json(listing):
               try:
                   if listing.search_type != 'rental':
                       return None
                   return listing.as_dict_for_mapping()
               except:
                   return None
        
           daft = Daft()
           daft.set_county("Dublin City")
           daft.set_listing_type(RentType.ANY)
           daft.set_max_price(2000)
           daft.set_min_beds(2)
           daft.set_max_beds(2)
        
           listings = daft.search()
           properties = []
           print("Translating {} listing object into json, it will take a few minutes".format(str(len(listings))))
           print("Igonre the error message")
        
           # time the translation
           start = time.time()
           properties = Parallel(n_jobs=6, prefer="threads")(delayed(translate_listing_to_json)(listing) for listing in listings)
           properties = [p for p in properties if p is not None] # remove the None
           end = time.time()
           print("Time for json translations {}s".format(end-start))
        
        Table of perfomance speedup for 501 listings Threads \| Time (s) \|
        Speedup ———— \| ————- \| ————- 1 \| 178 \| 1.0 2 \| 101 \| 1.8 3 \| 72
        \| 2.5 4 \| 61 \| 2.9 6 \| 54 \| 3.3
        
        Tests
        -----
        
        The Python unittest module contains its own test discovery function,
        which you can run from the command line:
        
        ::
        
            python -m unittest discover tests/
        
        Contributing
        ------------
        
        -  Fork the project and clone locally.
        -  Create a new branch for what you’re going to work on.
        -  Push to your origin repository.
        -  Create a new pull request in GitHub.
        
        .. |Build Status| image:: https://travis-ci.org/AnthonyBloomer/daftlistings.svg?branch=dev
           :target: https://travis-ci.org/AnthonyBloomer/daftlistings
        .. |codecov| image:: https://codecov.io/gh/AnthonyBloomer/daftlistings/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/AnthonyBloomer/daftlistings
        
Keywords: daft,web scraping,real estate,web scraper,daft.ie
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
